Enablers and Disablers for Contactless Payment Acceptance among Malaysian Adults

The paper examines contactless (cashless) payments—digital transactions via e-wallets, smartphones, or smart cards using technologies such as NFC. Despite rising awareness in Southeast Asia, usage lags; in Malaysia, 74% used contactless for retail purchases in 2022 (up from 56% in 2021), yet resistance persists despite government promotion. The study seeks to clarify consumer-side factors that enable or hinder contactless payment acceptance in a developing-country context. Research question: What are the key enablers and disablers for contactless payment acceptance among Malaysians? To address gaps where prior work emphasized TAM/UTAUT while underexploring TRI-based traits and factors like status or enjoyment, the authors develop an integrated Contactless Payment Acceptance Model (CPAM) to identify influential factors.

Theoretical background synthesizes major acceptance frameworks: TPB (attitude, subjective norms, perceived behavioral control), TAM (perceived usefulness, perceived ease of use), and UTAUT/UTAUT2 (performance/effort expectancy, social influence, facilitating conditions; extended with hedonic motivation, price value, habit). UTAUT2 has demonstrated higher predictive ability than its predecessor and TAM. Personality trait-based Technology Readiness Index (TRI 2.0) introduces enablers (optimism, innovativeness) and disablers (discomfort, insecurity), though less applied in contactless payment research. Building on these, CPAM partially adapts UTAUT2 (performance/effort expectancy and hedonic motivation retained; social influence, price, facilitating conditions excluded due to high smartphone penetration and focus on individual traits) and incorporates all TRI 2.0 traits. Additional literature-derived factors include lack of awareness, compatibility, and status symbol (treated as an individual factor distinct from social influence). Ten hypotheses were formulated: H1 perceived ease of use → acceptance (+); H2 perceived usefulness → acceptance (+); H3 optimism → acceptance (+); H4 compatibility → acceptance (+); H5 enjoyment → acceptance (+); H6 status symbol → acceptance (+); H7 innovativeness → acceptance (+); H8 lack of awareness → acceptance (−); H9 discomfort → acceptance (−); H10 perceived insecurity → acceptance (−).

Design: Cross-sectional survey using convenience sampling via emails (Google Form links) and social media (Facebook, WhatsApp). Minimum sample size estimated with G*Power for small effect size (f2=0.20), alpha=0.05, power=0.90 → 207; obtained N=434 valid responses. Participants: Adults aged 18–64; 246 females and 188 males. Age: 77.2% aged 25–50, 17.5% <25, 5.3% >51. Education: ~64% tertiary, 35.5% postgraduate, ~1% high school. Usage: 42.9% use contactless 4–9 times/week; 41.0% <3 times/week; 16.1% >10 times/week. 92.4% used contactless payments; popular services: GrabPay, Maybank QR Pay, JomPay, Boost. Instrument: Structured questionnaire with demographics (Part A) and constructs (Part B) covering enablers, disablers, and acceptance. Items measured on 4-point Likert scale (1=Strongly disagree to 4=Strongly agree). Pilot with 30 respondents indicated no major issues. Operational definitions aligned with UTAUT2, TRI 2.0, and prior literature. Analysis: Descriptive statistics (SPSS 27). Measurement and structural model assessed via SEM using AMOS 25. Thresholds: factor loadings ≥0.70 (Hair et al., 2014); Cronbach’s alpha and composite reliability ≥0.70; AVE ≥0.50. Discriminant validity via Fornell–Larcker criterion. Model fit assessed using χ²/df, CFI, GFI, RMSEA, SRMR (Hu & Bentler, 1998). Innovativeness and perceived insecurity constructs were removed due to low loadings (<0.60). Goodness-of-fit indices for final model: χ²/df=3.41 (p<0.001); CFI=0.928; GFI=0.901; RMSEA=0.075; SRMR=0.077, indicating acceptable fit.

• Measurement model: Reliability and validity established for retained constructs; discriminant validity satisfied. Innovativeness and perceived insecurity dropped due to low factor loadings.
• Structural model (SEM): CPAM explained 71.2% of the variance in contactless payment acceptance (R²=0.712).
• Significant predictors: • Perceived usefulness → acceptance: β=2.64, p=0.001, t=4.965 (positive). • Perceived ease of use → acceptance: β=2.06, p=0.001, t=3.750 (positive). • Lack of awareness → acceptance: β=−0.33, p=0.008, t=−2.649 (negative). • Discomfort → acceptance: β=−0.43, p=0.001, t=−3.582 (negative).
• Non-significant predictors: status symbol, compatibility, enjoyment, optimism (p>0.05).
• Model fit acceptable: χ²/df=3.41; CFI=0.928; GFI=0.901; RMSEA=0.075; SRMR=0.077.

Findings directly address the research question by identifying core enablers and barriers for Malaysian adults. Consistent with TAM/UTAUT literature, perceived usefulness and ease of use are primary drivers, indicating that clear performance benefits and low effort in using contactless systems are central to acceptance. The significant negative effects of lack of awareness and discomfort highlight informational and usability/friction barriers: insufficient knowledge about where/when/how to use contactless payments and perceived hassles (setup, maintaining balances, device dependency) reduce acceptance. Insignificance of status symbol, compatibility, enjoyment, and optimism may reflect the ubiquity and utilitarian nature of contactless payments in a largely young, educated, urban sample where such payments are commonplace tools rather than status markers or hedonic experiences. Taken together, the results suggest prioritizing intuitive design and visible utility, while tackling knowledge gaps and perceived hassles to expand adoption. Broader promotion and education by service providers and government can address awareness and trust, supporting diffusion in developing-country contexts.

The study develops and validates the Contactless Payment Acceptance Model (CPAM), integrating UTAUT2, TRI 2.0, and additional literature-derived factors, to explain acceptance among Malaysian adults. The model demonstrates high explanatory power (R²=71.2%). Key contributions include identifying perceived usefulness and perceived ease of use as dominant enablers, and lack of awareness and discomfort as significant disablers. The work refines theoretical understanding by combining trait-based readiness with technology acceptance constructs and highlighting underexplored barriers in developing contexts. Practically, it recommends designing user-friendly, functionally advantageous systems and running awareness/education initiatives to mitigate discomfort and knowledge deficits, thereby encouraging wider adoption. Future research should broaden samples demographically and geographically, revisit excluded or non-significant constructs (e.g., social influence, price, habit, innovativeness, perceived insecurity), and complement surveys with qualitative methods to uncover deeper motivations.

• Sampling bias: Predominantly urban, educated respondents obtained via online channels may limit generalizability. Low-income and older groups were underrepresented.
• Self-report bias: Self-administered questionnaires may introduce social desirability and reporting biases.
• Model scope: Partial adaptation of UTAUT2 excluded factors such as social influence, price, and habit; status symbol used as a proxy for image/social influence. Innovativeness and perceived insecurity were excluded post hoc due to low loadings. Several constructs (status symbol, compatibility, enjoyment, optimism) were insignificant, diverging from some prior studies.
• Future directions: Recruit more diverse socioeconomic groups (including smaller towns, lower education/income, older adults), consider qualitative methods (e.g., interviews), and re-examine excluded/insignificant constructs for a more comprehensive assessment.